Multi-Task Deep Residual Echo Suppression with Echo-aware Loss
Shimin Zhang, Ziteng Wang, Jiayao Sun, Yihui Fu, Biao Tian, Qiang Fu,, Lei Xie

TL;DR
This paper presents a multi-task deep residual echo suppression method using an echo-aware loss function, combining linear AEC and neural post-filter with TDE, achieving top rankings in the ICASSP 2022 AEC Challenge.
Contribution
It introduces a hybrid linear and neural echo suppression approach with a multi-task learning framework and an echo-aware loss, improving perceptual quality and echo suppression performance.
Findings
TDE module improves perceptual quality.
Using linear echo as input yields better results.
Adaptive filter convergence enhances performance.
Abstract
This paper introduces the NWPU Team's entry to the ICASSP 2022 AEC Challenge. We take a hybrid approach that cascades a linear AEC with a neural post-filter. The former is used to deal with the linear echo components while the latter suppresses the residual non-linear echo components. We use gated convolutional F-T-LSTM neural network (GFTNN) as the backbone and shape the post-filter by a multi-task learning (MTL) framework, where a voice activity detection (VAD) module is adopted as an auxiliary task along with echo suppression, with the aim to avoid over suppression that may cause speech distortion. Moreover, we adopt an echo-aware loss function, where the mean square error (MSE) loss can be optimized particularly for every time-frequency bin (TF-bin) according to the signal-to-echo ratio (SER), leading to further suppression on the echo. Extensive ablation study shows that the time…
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